In our study, we aimed to evaluate the complications after total gastrectomy by Clavien-Dindo classification and to determine the related risk factors.
Patients who underwent total gastrectomy due to gastric cancer between 2015-2019 were included in the study. Patients were divided into two groups according to postoperative complication classification Clavien Dindo, those with 3 or higher were Group 1 and the others were Group 2. Demographic and clinical features, laboratory parameters, tumor characteristics, postoperative results and mean survival were compared in the groups. Risk factors for Clavien Dindo 3 and above were analyzed by univariate analysis and multivariate logistic regression analysis.
A total of 104 patients participated in our study. https://www.selleckchem.com/products/td139.html Group1 consisted of 25 and Group2 consisted of 79 patients. Male sex was high in both groups (52% vs67.1%, p0.130). BMI (26 vs 23, p0.023), albumin (3.24 vs 3.51, p0,040), postoperative mortality (%28vs% 2.5, p0.001), postoperative duration of hospitalization (17.60vs9.25 days, p0.000) were different between the groups, but total survival (month) was not statistically significantly different (19.60vs18.53, p0.377). In multivariate analysis, tumor Stage 3C (OR =0.177,95% CI = (0.067-0.468), p0.000), operation duration ?240 min (OR =2.105,95% CI = (1.080-4.100, p0.029) and application of neoadjuvant treatment (HR =3.026,95%CI =(1.682-5.446), p0.000) were indepent risk factors DISCUSSION In conclusion, obesity, hypoalbuminemia, anemia, tumor stage, duration of operation, and taking neoadjuvant therapy were closely related to postoperative complications. Although the development of postoperative complication increased the length of hospitalization and postoperative mortality, it did not decrease survival in the long term.
Gastric cancer, Postoperative complication, Total gastrectomy.
Gastric cancer, Postoperative complication, Total gastrectomy.Innovative applications based on two-dimensional solids require cost-effective fabrication processes resulting in large areas of high quality materials. Chemical vapour deposition is among the most promising methods to fulfill these requirements. However, for 2D materials prepared in this way it is generally assumed that they are of inferior quality in comparison to the exfoliated 2D materials commonly used in basic research. In this work we challenge this assumption and aim to quantify the differences in quality for the prototypical transition metal dichalcogenide MoS2. To this end single layers of MoS2 prepared by different techniques (exfoliation, grown by different chemical vapour deposition methods, transfer techniques and as vertical heterostructure with graphene) are studied by Raman and photoluminescence spectroscopy, complemented by atomic force microscopy. We demonstrate that as-prepared MoS2, directly grown on SiO2, differs from exfoliated MoS2 in terms of higher photoluminescence, lower electron concentration and increased strain. As soon as a water film is intercalated (e.g. by transfer) underneath the grown MoS2, in particular the (opto)electronic properties become practically identical to those of exfoliated MoS2. A comparison of the two most common precursors shows that the growth with MoO3 causes greater strain and/or defect density deviations than growth with ammonium heptamolybdate. As part of a heterostructure directly grown MoS2 interacts much stronger with the substrate and in this case an intercalated water film does not lead to the complete decoupling, which is typical for exfoliation or transfer. Our work shows that the supposedly poorer quality of grown 2D transition metal dichalcogenides is indeed a misconception.As the global burden of cardiovascular disease increases, proactive cardiovascular healthcare by means of accurate, precise, continuous, and non-invasive monitoring is becoming crucial. However, no current device is able to provide cardiac hemodynamic monitoring with the aforementioned criterion. Electrical impedance tomography (EIT) is an inexpensive, non-invasive imaging modality that can provide real-time images of internal conductivity distributions that describe physiological activity. This work explores and compares a standard approach of regular cardiac gated averaging (RCGA) and a newly developed method, cardiac eigen-imaging (CEI), based on the singular value decomposition (SVD) to isolate cardiac activity in thoracic EIT.
EIT and heart-rate (HR) data were collected from 20 heart-failure patients preceding echocardiography. Features from RCGA and CEI images were correlated with stroke volume (SV) from echocardiography and image reconstruction parameters were optimized using leave-one-out (LOO) crtings.Stochastic neuromorphic computation (SNC) has the potential to enable a low power, error tolerant and scalable computing platform in comparison to its deterministic counterparts. However, the hardware implementation of complementary metal oxide semiconductor (CMOS)-based stochastic circuits involves conversion blocks that cost more than the actual processing circuits. The realization of the activation function for SNCs also requires a complicated circuit that results in a significant amount of power dissipation and area overhead. The inherent probabilistic switching behavior of nanomagnets provides an advantage to overcome these complexity issues for the realization of low power and area efficient SNC systems. This paper presents magnetic tunnel junction (MTJ)-based stochastic computing methodology for the implementation of a neural network. The stochastic switching behavior of the MTJ has been exploited to design a binary to stochastic converter to mitigate the complexity of the CMOS-based design. The paper also presents the technique for realizing stochastic sigmoid activation function using an MTJ. Such circuits are simpler than existing ones and use considerably less power. An image classification system employing the proposed circuits has been implemented to verify the effectiveness of the technique. The MTJ-based SNC system shows area and energy reduction by a factor of 13.5 and 2.5, respectively, while the prediction accuracy is 86.66%. Furthermore, this paper investigates how crucial parameters, such as stochastic bitstream length, number of hidden layers and number of nodes in a hidden layer, need to be set precisely to realize an efficient MTJ-based stochastic neural network (SNN). The proposed methodology can prove a promising alternative for highly efficient digital stochastic computing applications.